Evaluating and Characterizing Human Rationales

• URL: http://arxiv.org/abs/2010.04736v1
• Date: Fri, 9 Oct 2020 18:00:04 GMT
• Title: Evaluating and Characterizing Human Rationales
• Authors: Samuel Carton, Anirudh Rathore, Chenhao Tan
• Abstract summary: We find that human rationales do not necessarily perform well on automated metrics. We propose improved metrics to account for model-dependent baseline performance. Our work leads to actionable suggestions for evaluating and characterizing rationales.
• Score: 12.678505281794715
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Two main approaches for evaluating the quality of machine-generated rationales are: 1) using human rationales as a gold standard; and 2) automated metrics based on how rationales affect model behavior. An open question, however, is how human rationales fare with these automatic metrics. Analyzing a variety of datasets and models, we find that human rationales do not necessarily perform well on these metrics. To unpack this finding, we propose improved metrics to account for model-dependent baseline performance. We then propose two methods to further characterize rationale quality, one based on model retraining and one on using "fidelity curves" to reveal properties such as irrelevance and redundancy. Our work leads to actionable suggestions for evaluating and characterizing rationales.

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